Сontemporary Medical Decision Support Systems Based on Artificial Intelligence for the Analysis of Digital Mammographic Images-Reference-Cited by-同舟云学术

Сontemporary Medical Decision Support Systems Based on Artificial Intelligence for the Analysis of Digital Mammographic Images

Published:2023-08-07 Issue:2 Volume:104 Page:151-162
ISSN:2619-0478
Container-title:Journal of radiology and nuclear medicine
language:
Short-container-title:Vestn. rentgenol. radiol.

Author:

Solodkiy V. A.¹^ORCID,Kaprin A. D.²^ORCID,Nudnov N. V.³^ORCID,Kharchenko N. V.⁴^ORCID,Khodorovich O. S.¹^ORCID,Zapirov G. M.⁴^ORCID,Sherstneva T. V.¹^ORCID,Dibirova Sh. M.⁵^ORCID,Kanakhina L. B.¹^ORCID

Affiliation:

1. Russian Scientific Center of Roentgenoradiology

2. National Medical Research Center of Radiology; Peoples’ Friendship University of Russia

3. Russian Scientific Center of Roentgenoradiology Peoples’ Friendship University of Russia

4. Peoples’ Friendship University of Russia

5. Russian Scientific Center of Roentgenoradiology; Peoples’ Friendship University of Russia

Abstract

The relevance of implementing artificial intelligence (AI) technologies in the diagnosis of breast cancer (BC) is associated with a continuing high increase in BC incidence among women and its leading position in the structure of cancer incidence. Theoretically, using AI technologies is possible both at the stage of screening and in clarifying BC diagnosis. The article provides a brief overview of AI systems used in clinical practice and discusses their prospects in BC diagnosis. Advances in machine learning can be effective to improve the accuracy of mammography screening by reducing missed cancer cases and false positives.

Publisher

Luchevaya Diagnostika

Subject

General Medicine

Reference42 articles.

1. Kaprin AD, Starinsky VV, Shakhzadova AO (Eds). The state of oncological care for the population of Russia in 2021. Мoscow; 2022: 239 pp. (in Russ.).

2. Ghoncheh M, Pournamdar Z, Salehiniya H. Incidence and mortality and epidemiology of breast cancer in the world. Asian Pac J Cancer Prev. 2016; 17(S3): 43–6. http://doi.org/10.7314/apjcp.2016.17.s3.43.

3. Yassin NIR, Omran S, El Houby EMF, Allam H. Machine learning techniques for breast cancer computer aided diagnosis using different image modalities: a systematic review. Comput Meth Progr Biomed. 2018; 156: 25–45. http://doi.org/10.1016/j.cmpb.2017.12.012.

4. Harvey H, Heindl A, Khara G, et al. Deep learning in breast cancer screening. In: Ranschaert ER, Morozov S, Algra PR (Еds). Artificial intelligence in medical imaging. Springer; 2019: 187–215.

5. Harvey H, Karpati E, Khara G, et al. The role of deep learning in breast screening. Curr Breast Cancer Rep. 2019; 11(1): 17–22. https://doi.org/10.1007/s12609-019-0301-7.

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